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KBGWO-RNP:Knowledge-Based GreyWolf Optimizer for Multi-Criteria RFID Network Planning in Medical Asset Monitoring 认领 引用
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作者 Mohamad Khairi Ishak Samir Ait Lhadj Lamin +4 位作者 Mohammad Shokouhifar Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computers, Materials & Continua》 SCIE EI 2026年第9期2253-2282,共30页
Radio Frequency Identification(RFID)has emerged as an effective remote technology for real-time monitoring and management of medical assets in hospitals.Most existing RFID Network Planning(RNP)methods are primarily ba... Radio Frequency Identification(RFID)has emerged as an effective remote technology for real-time monitoring and management of medical assets in hospitals.Most existing RFID Network Planning(RNP)methods are primarily based on either heuristic or metaheuristic approaches.While heuristic approaches are computationally efficient and converge rapidly,they often suffer from premature convergence and suboptimal network configurations.Conversely,metaheuristic algorithms provide stronger global search capabilities and improved solution quality,but they typically require higher computational effort and may still exhibit stagnation in local optima when applied to complex hospital layouts.To overcome these limitations while utilizing the strengths of both paradigms,this paper proposes a Knowledge-Based Grey Wolf Optimizer for RNP,referred to as KBGWO-RNP.The proposed method integrates the global exploration capability of the metaheuristic-driven search with knowledge-based heuristic operators that guide local search and refinement.In particular,the framework incorporates domain-specific knowledge to enhance antenna placement decisions and improve convergence behavior.The KBGWO-RNP framework supports directional antennas with varying coverage profiles.A multi-criteria objective function is formulated to increase the network coverage while simultaneously reducing inter-antenna interference and deployment cost.Extensive simulation experiments conducted on a hospital layout demonstrate that the proposed KBGWO-RNP framework consistently outperforms conventional heuristic and metaheuristic baselines.The results show that the proposed method achieves a coverage rate of 90.4%while maintaining the interference level at 19.9%,indicating a strong balance between performance different objectives.Furthermore,ablation analysis confirms that the integration of knowledge-based guidance with metaheuristic search significantly improves both solution quality and stability.The proposed framework offers a balanced trade-off between computational efficiency and optimization performance,and demonstrating clear advantages over existing approaches. 展开更多
关键词 RFID network planning(RNP) medical asset tracking coverage interference heuristic information grey wolf optimizer(GWO)
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Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks:A Methodological Survey 认领 引用
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作者 Mohammad Shokouhifar Fakhrosadat Fanian +4 位作者 Mehdi Hosseinzadeh Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期191-255,共65页
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw... Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field. 展开更多
关键词 Wireless sensor networks data transmission energy efficiency lifetime clustering routing optimization metaheuristic algorithms grey wolf optimizer
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Localization of Acoustic Emission Source in Rock Using SMIGWO Algorithm 认领 引用 被引量：2
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作者 Jiong Wei Fuqiang Gao +2 位作者 Jinfu Lou Lei Yang Xiaoqing Wang 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2025年第2期42-51,共10页
The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and con... The Grey Wolf Optimization(GWO)algorithm is acknowledged as an effective method for rock acoustic emission localization.However,the conventional GWO algorithm encounters challenges related to solution accuracy and convergence speed.To address these concerns,this paper develops a Simplex Improved Grey Wolf Optimizer(SMIGWO)algorithm.The randomly generating initial populations are replaced with the iterative chaotic sequences.The search process is optimized using the convergence factor optimization algorithm based on the inverse incompleteГfunction.The simplex method is utilized to address issues related to poorly positioned grey wolves.Experimental results demonstrate that,compared to the conventional GWO algorithm-based AE localization algorithm,the proposed algorithm achieves a higher solution accuracy and showcases a shorter search time.Additionally,the algorithm demonstrates fewer convergence steps,indicating superior convergence efficiency.These findings highlight that the proposed SMIGWO algorithm offers enhanced solution accuracy,stability,and optimization performance.The benefits of the SMIGWO algorithm extend universally across various materials,such as aluminum,granite,and sandstone,showcasing consistent effectiveness irrespective of material type.Consequently,this algorithm emerges as a highly effective tool for identifying acoustic emission signals and improving the precision of rock acoustic emission localization. 展开更多
关键词 Acoustic emission Source localization Iterative chaotic mapping Simplex method Grey wolf optimizer algorithm
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Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm 认领 引用 被引量：9
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作者 Xue Wang Zhanshan Li +2 位作者 Heng Kang Yongping Huang Di Gai 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第3期711-720,共10页
Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PC... Medical image segmentation is a challenging task especially in multimodality medical image analysis.In this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image segmentation.Specifically,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image segmentation.The image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy region.In the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the image.Two modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN algorithm.The experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators. 展开更多
关键词 grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation
改进LGWO和变步长P&O的光伏阵列功率跟踪策略 认领 引用 被引量：3
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作者 刘宝宏 陈斌 +2 位作者 钱立峰 史一诺 吕莉 《电源学报》 CSCD 北大核心 2026年第4期110-121,共12页
针对局部阴影情况下光伏阵列输出功率多峰值导致传统灰狼优化算法跟踪最大功率点收敛速度慢、搜索精度低且易陷入局部最优的问题,提出1种嵌入莱维飞行的改进灰狼优化和变步长扰动观察法算法。全局搜索中提出的新型非线性收敛因子提升了... 针对局部阴影情况下光伏阵列输出功率多峰值导致传统灰狼优化算法跟踪最大功率点收敛速度慢、搜索精度低且易陷入局部最优的问题,提出1种嵌入莱维飞行的改进灰狼优化和变步长扰动观察法算法。全局搜索中提出的新型非线性收敛因子提升了灰狼优化算法的收敛速度和搜索精度,通过初始化种群正态分布进一步提升灰狼搜索效率;嵌入莱维飞行增加了全局搜索随机性,避免了搜索过程陷入局部最优;局部搜索中通过变步长扰动观察法实现全局最大功率点快速捕获。为验证算法的有效性,构建了已发表灰狼优化GWO(grey wolf optimization)算法和嵌入莱维飞行的改进GWO和变步长扰动观察ILGWO-VP&O(improved Levy-flight GWO and variable-step perturbation&observation)算法的光伏发电系统,并进行实验验证。实验结果表明,提出的ILGWO-VP&O算法动态响应速度最快,稳态控制精度最优。 展开更多
关键词 光伏发电系统 局部阴影 最大功率点跟踪 改进灰狼优化算法 变步长扰动观察法 莱维飞行策略
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基于Lasso-GWO-RF模型的长江上游交通碳排放预测 认领 引用 被引量：1
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作者 焦柳丹 王艺洁 +1 位作者 霍小森 吴柳 《重庆交通大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第4期54-60,共7页
长江上游地区作为我国“双碳”战略实施与长江经济带生态屏障建设的关键区域,其交通运输业碳排放治理对实现“减污降碳协同增效”目标具有重要作用。基于2000—2021年重庆、贵州、四川、云南这四省市的数据,运用Lasso回归对STIRPAT模型... 长江上游地区作为我国“双碳”战略实施与长江经济带生态屏障建设的关键区域,其交通运输业碳排放治理对实现“减污降碳协同增效”目标具有重要作用。基于2000—2021年重庆、贵州、四川、云南这四省市的数据,运用Lasso回归对STIRPAT模型的多重共线性进行处理,构建融合灰狼优化算法与随机森林(GWO-RF)的碳排放预测模型,并进行多情景分析。研究结果显示:在基准情景与低碳情景下,该地区交通运输业碳排放预计于2032年达峰,峰值分别为109.19、104.08 Mt CO2;在高碳情景下,达峰时间将推迟至2034年,峰值上升至117.51 Mt CO2。2022—2040年间,该地区交通运输业碳排放总体呈现“先增长后达峰,随后逐步趋稳下降”的演变路径。该成果可为跨区域交通运输业碳排放精准预测与达峰路径设计提供方法参考与决策依据。 展开更多
关键词 交通工程 碳排放 机器学习 情景分析 灰狼优化算法(GWO)
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VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity 认领 引用
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作者 Junqiang Jiang Zhifang Sun +3 位作者 Xiong Jiang Shengjie Jin Yinli Jiang Bo Fan 《Computers, Materials & Continua》 SCIE EI 2023年第11期1617-1644,共28页
The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple pr... The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf hunting.Along with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these disadvantages.However,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence.To solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this study.VGWO first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering cases.For 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value. 展开更多
关键词 Intelligence optimization algorithm grey wolf optimizer(GWO) manhattan distance symmetric coordinates
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Prediction of Backfill Strength Based on Support Vector Regression Improved by Grey Wolf Optimization 认领 引用
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作者 张博 李克庆 +2 位作者 胡亚飞 吉坤 韩斌 《Journal of Shanghai Jiaotong university(Science)》 EI 2023年第5期686-694,共9页
In order to predict backfill strength rapidly with high accuracy and provide a new technical support for digitization and intelligentization of mine,a support vector regression(SVR)model improved by grey wolf optimiza... In order to predict backfill strength rapidly with high accuracy and provide a new technical support for digitization and intelligentization of mine,a support vector regression(SVR)model improved by grey wolf optimization(GWO),GWO-SVR model,is established.First,GWO is used to optimize penalty term and kernel function parameter in SVR model with high accuracy based on the experimental data of uniaxial compressive strength of filling body.Subsequently,a prediction model which uses the best two parameters of best c and best g is established with the slurry density,cement dosage,ratio of artificial aggregate to tailings,and curing time taken as input factors,and uniaxial compressive strength of backfill as the output factor.The root mean square error of this GWO-SVR model in predicting backfill strength is 0.143 and the coefficient of determination is 0.983,which means that the predictive effect of this model is accurate and reliable.Compared with the original SVR model without the optimization of GWO and particle swam optimization(PSO)-SVR model,the performance of GWO-SVR model is greatly promoted.The establishment of GWO-SVR model provides a new tool for predicting backfill strength scientifically. 展开更多
关键词 underground mining backfill strength prediction model grey wolf optimization(GWO) support vector regression(SVR)
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基于改进CS-GWO算法的多无人机三维路径规划 认领 引用
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作者 宋宇 赵桐 《长春工业大学学报》 CAS 2026年第2期147-152,共6页
随着无人机技术的迅速发展,多无人机协同任务在各个领域中引起了广泛关注。然而,在复杂环境下,实现多无人机协同避障并成功完成任务,但仍面临突出挑战。为了解决这一问题,提出一种基于结合布谷鸟搜索(CS)算法和灰狼优化(GWO)算法的多无... 随着无人机技术的迅速发展,多无人机协同任务在各个领域中引起了广泛关注。然而,在复杂环境下,实现多无人机协同避障并成功完成任务,但仍面临突出挑战。为了解决这一问题,提出一种基于结合布谷鸟搜索(CS)算法和灰狼优化(GWO)算法的多无人机协同避障路径规划方法。该方法利用GWO的全局搜索能力和CS的随机性,综合考虑了路径长度、飞行高度和路径平滑性等多个关键因素,设计了一个新的适应度函数,进而通过改进算法实现高效路径规划。根据仿真结果可以得出,所提出的方法有效地达成了多无人机协同避障并完成任务的目标,为多无人机系统的应用提供了新的研究思路和解决方案。 展开更多
关键词 无人机路径规划 三维避障 布谷鸟搜索算法 灰狼优化算法
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A Grey Wolf Optimization-Based Tilt Tri-rotor UAV Altitude Control in Transition Mode 认领 引用 被引量：3
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作者 MA Yan WANG Yingxun +2 位作者 CAI Zhihao ZHAO Jiang LIU Ningjun 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2022年第2期186-200,共15页
To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt ... To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme. 展开更多
关键词 tilt tri-rotor unmanned aerial vehicle altitude control neural network adaptive control grey wolf optimization(GWO)
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基于GWO-XGBoost模型的致密砂岩储层流体测井智能识别——以鄂尔多斯盆地洪德地区三叠系长8段为例 认领 引用 被引量：1
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作者 薛博文 张兆辉 +2 位作者 张皎生 邹建栋 张闻亭 《岩性油气藏》 CAS CSCD 北大核心 2026年第2期111-121,共11页
针对传统测井解释方法在致密砂岩储层流体类型上识别精度低的问题,提出了一种基于测井曲线的GWO-XGBoost模型储层流体智能识别方法,并将该方法应用于鄂尔多斯盆地洪德地区三叠系长8段致密砂岩储层中。研究结果表明:①以鄂尔多斯盆地洪... 针对传统测井解释方法在致密砂岩储层流体类型上识别精度低的问题,提出了一种基于测井曲线的GWO-XGBoost模型储层流体智能识别方法,并将该方法应用于鄂尔多斯盆地洪德地区三叠系长8段致密砂岩储层中。研究结果表明:①以鄂尔多斯盆地洪德地区三叠系长8段实际试油数据为目标变量,经主成分分析法优选出声波、自然电位、密度、井径、中子、自然伽马、电阻率测井(AT20、AT60和AT90)等9条测井曲线作为特征参数,再通过灰狼优化算法(GWO)对XGBoost模型的关键超参数进行全局优化。②GWO-XGBoost模型对储层流体类型的识别准确率达到96.55%,相较于XGBoost、随机森林(RF)和支持向量机(SVM)模型,其识别精度分别提升了6.03%,6.89%和22.41%,展现出明显的优势。③实际单井应用中,GWO-XGBoost模型通过对多维测井响应特征的综合分析与非线性特征学习,能够有效解决人工解释中低阻油层与高阻水层易混淆的难题,该模型在复杂储层条件下具有较高的稳定性与可靠性,可为提高致密砂岩油气勘探开发效率提供技术支撑。 展开更多
关键词 XGBoost 灰太狼算法(GWO) 智能模型 储层流体识别 致密砂岩 非常规油气 三叠系 洪德地区 鄂尔多斯盆地
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Solving high-dimensional global optimization problems via solution space restructuring with neural network 认领 引用
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作者 N.VO T.LE-DUC +3 位作者 H.TANG H.NGUYEN-XUAN S.H.LEE J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1383-1400,共18页
It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-fre... It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-free techniques.However,the general framework for adaptively dealing with a particular optimization problem is commonly overlooked and thus still hidden in the literature.To overcome this limitation,a new approach assisted by the neural network(NN)is proposed for solving high-dimensional optimization issues.By restructuring the search space to optimize the objective function via a nonlinear mapping constructed by an autoencoder(AE),the surrogate solution space is constructed by a network training process and dynamically oriented to the optimal solution of the optimization issue.To enhance the optimization efficiency and address non-smooth problems,the classical metaheuristic grey wolf optimizer(GWO)and the adaptive moment estimation(Adam)are sequentially employed to complement the disadvantages of the constituted models.The effectiveness of the proposed approach is validated by solving a set of mathematical functions with 1000-dimensional and three large-scale truss design optimization problems.Several numerical experiments show that the solution space is reduced in terms of both size and complexity based on the restructuring procedure,in which the global optimal solution is still conserved,leading to better optimization efficiency when solving optimization problems with complex search domains with large dimensions.In addition,the hybrid optimizer has also been proven to be more effective when combined with the restructuring technique owing to the use of the Adam algorithm in the second phase. 展开更多
关键词 high-dimensional optimization solution space restructuring adaptive moment estimation(Adam) grey wolf optimizer(GWO) neural network(NN)
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Application of interval type-2 TSK FLS method based on IGWO algorithm in short-term photovoltaic power forecasting 认领 引用 被引量：1
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作者 LI Jun ZENG Yuxiang 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2025年第2期258-271,共14页
For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compare... For short-term PV power prediction,based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems(IT2 TSK FLS),combined with improved grey wolf optimizer(IGWO)algorithm,an IGWO-IT2 TSK FLS method was proposed.Compared with the type-1 TSK fuzzy logic system method,interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation(BP)algorithm,and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model.By improving the gray wolf optimization algorithm,the early convergence judgment mechanism,nonlinear cosine adjustment strategy,and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum.The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance.Under the same conditions,it was also compared with different IT2 TSK FLS methods,such as type I TSK FLS method,BP algorithm,genetic algorithm,differential evolution,particle swarm optimization,biogeography optimization,gray wolf optimization,etc.Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance,showing its effectiveness and application potential. 展开更多
关键词 photovoltaic power interval type-2 fuzzy logic system grey wolf optimizer algorithm forecast performance of model
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GWO优化CNN的短电弧铣削电极损耗状态监测模型 认领 引用
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作者 牛巧茹 周建平 +3 位作者 许燕 宋洁 范俊伟 连国禹 《机床与液压》 北大核心 2026年第7期119-124,共6页
在短电弧铣削加工过程中,电极损耗严重影响了加工精度和效率。为了实现对电极损耗状态的有效监测,提出一种基于灰狼寻优算法(GWO)与卷积神经网络(CNN)的智能监控模型。构建交互特征,对输入特征采用Min-Max标准化处理,对输出损耗量采用Z-... 在短电弧铣削加工过程中,电极损耗严重影响了加工精度和效率。为了实现对电极损耗状态的有效监测,提出一种基于灰狼寻优算法(GWO)与卷积神经网络(CNN)的智能监控模型。构建交互特征,对输入特征采用Min-Max标准化处理,对输出损耗量采用Z-score标准化处理,提升模型稳定性与泛化性能。通过构建包含特征交互层和多尺度卷积结构的CNN模型,结合GWO算法对网络超参数进行全局优化,实现电极磨损量的高精度预测。构建短电弧铣削工艺数据集,对钛合金铣削过程中电极损耗状态进行全面评估,筛选性能最优的组合模型,并与其他模型进行对比,验证所提方法的有效性。结果表明:该模型在综合性能上表现最优,R2达到97.9%,验证了所提方法的准确性和可行性。 展开更多
关键词 短电弧铣削 电极损耗 状态监测 灰狼优化(GWO)算法 卷积神经网络(CNN)
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基于GWO-SSA混合算法的绳驱动蛇形臂结构优化设计 认领 引用
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作者 夏楷捷 孙国瑞 汤腾飞 《轻工机械》 CAS 2026年第1期19-29,共11页
针对现有蛇形臂机器人尺度优化困难、单一元启发算法存在局限性等问题,课题组提出一种具有12自由度的绳驱动蛇形臂机器人及组合优化算法。采用具有2自由度(绕垂直轴旋转(Yaw)和绕横轴旋转(Pitch))的万向节关节结构,实现蛇形臂的灵活运动... 针对现有蛇形臂机器人尺度优化困难、单一元启发算法存在局限性等问题,课题组提出一种具有12自由度的绳驱动蛇形臂机器人及组合优化算法。采用具有2自由度(绕垂直轴旋转(Yaw)和绕横轴旋转(Pitch))的万向节关节结构,实现蛇形臂的灵活运动;基于D-H参数法与数值优化方法建立正/逆运动学模型,并利用蒙特卡洛法与网格搜索方法求解工作空间;提出融合灰狼优化算法(Grey Wolf Optimizer, GWO)与麻雀搜索算法(Sparrow Search Algorithm, SSA)的自适应混合优化策略,引入基于种群分布多样性的动态切换机制,以优化蛇形臂结构。研究结果表明:在受限工作场景下,蛇形臂可达工作空间体积提升了30%。课题组研制的绳驱动蛇形臂机器人结构轻便、模块化程度高,所提出的混合算法在收敛精度与稳定性方面均表现更优。 展开更多
关键词 蛇形臂机器人 万向节结构 绳驱动 工作空间 灰狼优化算法 麻雀搜索算法
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基于IGWO-VINC的光伏发电多峰值MPPT 认领 引用
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作者 贾莹 李永乐 《吉林大学学报（信息科学版）》 CAS 2026年第1期52-60,共9页
针对局部遮阴情况(PSC:Partial Shading Conditions)下光伏阵列输出功率呈现多峰值特征导致传统最大功率点跟踪(MPPT:Maximum Power Point Tracking)算法存在跟踪速度慢、跟踪精度低等问题,提出一种基于改进灰狼优化算法(GWO:Grey Wolf ... 针对局部遮阴情况(PSC:Partial Shading Conditions)下光伏阵列输出功率呈现多峰值特征导致传统最大功率点跟踪(MPPT:Maximum Power Point Tracking)算法存在跟踪速度慢、跟踪精度低等问题,提出一种基于改进灰狼优化算法(GWO:Grey Wolf Optimizer)和变步长电导增量法(VINC:Variable step-size Incremental Conductance)相结合的复合算法。首先,通过分析峰值点对应电压位置,在灰狼优化算法中加入峰值电压初始化策略;其次,引入非线性收敛因子以提升灰狼优化算法的全局搜索能力。该复合算法先利用改进灰狼优化算法进行全局搜索,再切换至引入dP/dU的变步长电导增量法进行局部搜索。Matlab/Simulink仿真结果表明,所提复合算法在静态和动态局部遮阴情况下均能提升跟踪速度与精度,同时减小输出功率振荡幅度。 展开更多
关键词 光伏发电 局部遮阴情况 最大功率点跟踪 灰狼优化算法 电导增量法
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Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance 认领 引用
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作者 Mahmoud Khatab Mohamed El-Gamel +2 位作者 Ahmed I. Saleh Asmaa H. Rabie Atallah El-Shenawy 《Open Journal of Optimization》 2024年第1期21-30,共10页
Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of ... Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms. 展开更多
关键词 Grey Wolf Optimization (GWO) Metaheuristic Algorithm Optimization Problems Agents’ Positions Leader Wolves Optimal Fitness Values Optimization Challenges
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Grey Wolf Optimizer to Real Power Dispatch with Non-Linear Constraints 认领 引用 被引量：2
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作者 G.R.Venkatakrishnan R.Rengaraj S.Salivahanan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2018年第4期25-45,共21页
A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimizati... A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems. 展开更多
关键词 Grey wolf optimization(GWO) constraints power generation dispatch evolutionary computation computational complexity algorithms
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基于GWO-BP模型与MOMPA算法的插秧机车架轻量化设计 认领 引用 被引量：4
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作者 陈岁繁 侯万森 +3 位作者 张浩南 李其朋 夏琪玮 陈问池 《机电工程》 CAS 北大核心 2025年第5期933-944,共12页
为实现水稻插秧机车架的轻量化目标,提出了基于灰狼优化反向传播神经网络(GWO-BP)模型与多目标海洋捕食者算法(MOMPA)的联合优化方法。首先,对GWO-BP模型与MOMPA优化算法的构建进行了理论分析,建立了车架的三维模型和有限元模型,并对其... 为实现水稻插秧机车架的轻量化目标,提出了基于灰狼优化反向传播神经网络(GWO-BP)模型与多目标海洋捕食者算法(MOMPA)的联合优化方法。首先,对GWO-BP模型与MOMPA优化算法的构建进行了理论分析,建立了车架的三维模型和有限元模型,并对其性能进行了仿真;然后,采用灵敏度分析确定了可作为优化设计变量的8个主要结构参数,并利用实验设计的方法计算出设计变量与目标参数之间响应关系的数据,从而建立了GWO-BP近似模型,联合近似模型与MOMPA优化算法,以车架质量、最大变形最小为优化目标,求出了轻量化车架的最优结构参数组合;最后,对车架优化结果进行了验证,同时,分析了车架模态性能,并建立了车架样机,通过试验验证了车架轻量化结果。研究结果表明:车架质量、车架最大变形和最大等效应力的拟合精度分别为0.998 8、0.987 8、0.986 7,建立的近似模型具有较高精度;优化后车架质量比原车架降低了9.26%;优化结果与仿真结果误差在2%以内,且优化后车架固有频率可以有效避开外界激励,通过对比优化前后车架质量及性能,确定了优化结果的准确性与有效性;根据优化结果制造了轻量化车架的样机,其整体质量较原车架减轻了10.3%,达到了良好的轻量化效果,为农机车架轻量化研究提供了一定的借鉴。 展开更多
关键词 水稻插秧机 轻量化 灰狼优化反向传播神经网络 多目标海洋捕食者优化算法 车架模态分析
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GWO优化CNN-BiLSTM-Attenion的轴承剩余寿命预测方法 认领 引用 被引量：20
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作者 李敬一 苏翔 《振动与冲击》 EI CSCD 北大核心 2025年第2期321-332,共12页
滚动轴承作为机械设备的重要部件,对其进行剩余使用寿命预测在企业的生产过程中变得越来越重要。目前,虽然主流的卷积神经网络(convolutional neural network, CNN)可以自动地从轴承的振动信号中提取特征,却不能给特征分配不同的权重来... 滚动轴承作为机械设备的重要部件,对其进行剩余使用寿命预测在企业的生产过程中变得越来越重要。目前,虽然主流的卷积神经网络(convolutional neural network, CNN)可以自动地从轴承的振动信号中提取特征,却不能给特征分配不同的权重来提高模型对重要特征的关注程度,对于长时间序列容易丢失重要信息。另外,神经网络中隐藏层神经元个数、学习率以及正则化参数等超参数还需要依靠人工经验设置。为了解决上述问题,提出基于灰狼优化(grey wolf optimizer, GWO)算法、优化集合CNN、双向长短期记忆(bidirectional long short term memory, BiLSTM)网络和注意力机制(Attention)轴承剩余使用寿命预测方法。首先,从原始振动信号中提取时域、频域以及时频域特征指标构建可选特征集;然后,通过构建考虑特征相关性、鲁棒性和单调性的综合评价指标筛选出高于设定阈值的轴承退化敏感特征集,作为预测模型的输入;最后,将预测值和真实值的均方误差作为GWO算法的适应度函数,优化预测模型获得最优隐藏层神经元个数、学习率和正则化参数,利用优化后模型进行剩余使用寿命预测,并在公开数据集上进行验证。结果表明,所提方法可在非经验指导下获得最优的超参数组合,优化后的预测模型与未进行优化模型相比,平均绝对误差与均方根误差分别降低了28.8%和24.3%。 展开更多
关键词 灰狼优化(GWO)算法 卷积神经网络(CNN) 双向长短期记忆(BiLSTM)网络 自注意力机制 剩余使用寿命预测
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